Simulation-based inference for AGN jet population modelling: Towards more robust comparisons of black hole jet speeds

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ID: 324609
2026
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Abstract
Abstract We present the most complete modelling of the MOJAVE 1.5 Jansky Quarter Century active galactic nuclei (AGN) jet population, using likelihood-free simulation-based inference. Due to the complex impact of a flux-limit on observed AGN data sets, careful modelling of the parent population is required. In particular, when observing and fitting to multiple data distributions likelihoods become non-intuitive. Parameter degeneracies further complicate the problem and make it suitable for likelihood-free, simulation-based inference. This method relies on a normalising flow learning the likelihood surface or posteriors directly. We extensively validate the flow to show that previous parameter estimates for the AGN jet speed distributions underestimated parameter errors significantly and do not capture the non-gaussianity of the parameter posteriors. The new results enable a better statistical comparison to other AGN population studies, but also a more accurate comparison of supermassive black hole jets with their lower mass counterparts, X-ray binaries (XRB). We find that the AGN follow a Lorentz factor distribution of the shape N(Γ)∝Γb with $b= -1.32_{-0.19}^{+0.20}$. This slope is consistent with the XRB Lorentz factor distribution at 2σ. Simulation-based inference as a method is generally well-suited to many astrophysical problems, and this paper shows the convenient applicability of this methodology to parent population studies of jetted AGN with multiple observables specifically.
Reference Key
openalex_W7202160396 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Clara Lilje, James Matthews, Rob Fender
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1515
URL
https://doi.org/10.1093/mnras/stag1515
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